{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| [08_machine_translation/02_transformer翻译模型.ipynb](https://github.com/shibing624/nlp-tutorial/tree/main/08_machine_translation/02_transformer翻译模型.ipynb)  | 从头实现Transformer翻译模型  |[Open In Colab](https://colab.research.google.com/github/shibing624/nlp-tutorial/blob/main/08_machine_translation/02_transformer翻译模型.ipynb) |\n",
    "\n",
    "# transformer翻译模型\n",
    "\n",
    "\n",
    "In this notebook we will be implementing a (slightly modified version) of the Transformer model from the [Attention is All You Need](https://arxiv.org/abs/1706.03762) paper. All images in this notebook will be taken from the Transformer paper. For more information about the Transformer, [see](https://www.mihaileric.com/posts/transformers-attention-in-disguise/) [these](https://jalammar.github.io/illustrated-transformer/) [three](http://nlp.seas.harvard.edu/2018/04/03/attention.html) articles.\n",
    "\n",
    "![](assets/transformer1.png)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "Similar to the Convolutional Sequence-to-Sequence model, the Transformer does not use any recurrence. It also does not use any convolutional layers. Instead the model is entirely made up of linear layers, attention mechanisms and normalization. \n",
    "\n",
    "As of January 2020, Transformers are the dominant architecture in NLP and are used to achieve state-of-the-art results for many tasks and it appears as if they will be for the near future. \n",
    "\n",
    "The most popular Transformer variant is [BERT](https://arxiv.org/abs/1810.04805) (**B**idirectional **E**ncoder **R**epresentations from **T**ransformers) and pre-trained versions of BERT are commonly used to replace the embedding layers - if not more - in NLP models. \n",
    "\n",
    "A common library used when dealing with pre-trained transformers is the [Transformers](https://huggingface.co/transformers/) library, see [here](https://huggingface.co/transformers/pretrained_models.html) for a list of all pre-trained models available.\n",
    "\n",
    "The differences between the implementation in this notebook and the paper are:\n",
    "- we use a learned positional encoding instead of a static one\n",
    "- we use the standard Adam optimizer with a static learning rate instead of one with warm-up and cool-down steps\n",
    "- we do not use label smoothing\n",
    "\n",
    "We make all of these changes as they closely follow BERT's set-up and the majority of Transformer variants use a similar set-up."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data\n",
    "\n",
    "As always, let's import all the required modules and set the random seeds for reproducability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import torchtext\n",
    "from torchtext.datasets import TranslationDataset, Multi30k\n",
    "from torchtext.data import Field, BucketIterator\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import random\n",
    "import math\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "torch.manual_seed(SEED)\n",
    "torch.cuda.manual_seed(SEED)\n",
    "torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll then create our tokenizers as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tokenize_de(text):\n",
    "    \"\"\"\n",
    "    Tokenizes German text from a string into a list of strings\n",
    "    \"\"\"\n",
    "    return [w for w in text.split() if w]\n",
    "\n",
    "def tokenize_en(text):\n",
    "    \"\"\"\n",
    "    Tokenizes English text from a string into a list of strings\n",
    "    \"\"\"\n",
    "    return [w for w in text.split() if w]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our fields are the same as the previous notebook. The model expects data to be fed in with the batch dimension first, so we use `batch_first = True`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC = Field(tokenize=tokenize_de,\n",
    "            init_token='<sos>',\n",
    "            eos_token='<eos>',\n",
    "            lower=True,\n",
    "            batch_first=True)\n",
    "\n",
    "TRG = Field(tokenize=tokenize_en,\n",
    "            init_token='<sos>',\n",
    "            eos_token='<eos>',\n",
    "            lower=True,\n",
    "            batch_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then load the Multi30k dataset and build the vocabulary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data, valid_data, test_data = Multi30k.splits(exts=('.de', '.en'),\n",
    "                                                    fields=(SRC, TRG))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC.build_vocab(train_data, min_freq = 2)\n",
    "TRG.build_vocab(train_data, min_freq = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we define the device and the data iterator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('device:', device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 128\n",
    "\n",
    "train_iterator, valid_iterator, test_iterator = BucketIterator.splits(\n",
    "    (train_data, valid_data, test_data), \n",
    "     batch_size = BATCH_SIZE,\n",
    "     device = device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Model\n",
    "\n",
    "Next, we'll build the model. Like previous notebooks it is made up of an *encoder* and a *decoder*, with the encoder *encoding* the input/source sentence (in German) into *context vector* and the decoder then *decoding* this context vector to output our output/target sentence (in English). \n",
    "\n",
    "### Encoder\n",
    "\n",
    "Similar to the ConvSeq2Seq model, the Transformer's encoder does not attempt to compress the entire source sentence, $X = (x_1, ... ,x_n)$, into a single context vector, $z$. Instead it produces a sequence of context vectors, $Z = (z_1, ... , z_n)$. So, if our input sequence was 5 tokens long we would have $Z = (z_1, z_2, z_3, z_4, z_5)$. Why do we call this a sequence of context vectors and not a sequence of hidden states? A hidden state at time $t$ in an RNN has only seen tokens $x_t$ and all the tokens before it. However, each context vector here has seen all tokens at all positions within the input sequence.\n",
    "\n",
    "![](assets/transformer-encoder.png)\n",
    "\n",
    "First, the tokens are passed through a standard embedding layer. Next, as the model has no recurrent it has no idea about the order of the tokens within the sequence. We solve this by using a second embedding layer called a *positional embedding layer*. This is a standard embedding layer where the input is not the token itself but the position of the token within the sequence, starting with the first token, the `<sos>` (start of sequence) token, in position 0. The position embedding has a \"vocabulary\" size of 100, which means our model can accept sentences up to 100 tokens long. This can be increased if we want to handle longer sentences.\n",
    "\n",
    "The original Transformer implementation from the Attention is All You Need paper does not learn positional embeddings. Instead it uses a fixed static embedding. Modern Transformer architectures, like BERT, use positional embeddings instead, hence we have decided to use them in these tutorials. Check out [this](http://nlp.seas.harvard.edu/2018/04/03/attention.html#positional-encoding) section to read more about the positional embeddings used in the original Transformer model.\n",
    "\n",
    "Next, the token and positional embeddings are elementwise summed together to get a vector which contains information about the token and also its position with in the sequence. However, before they are summed, the token embeddings are multiplied by a scaling factor which is $\\sqrt{d_{model}}$, where $d_{model}$ is the hidden dimension size, `hid_dim`. This supposedly reduces variance in the embeddings and the model is difficult to train reliably without this scaling factor. Dropout is then applied to the combined embeddings.\n",
    "\n",
    "The combined embeddings are then passed through $N$ *encoder layers* to get $Z$, which is then output and can be used by the decoder.\n",
    "\n",
    "The source mask, `src_mask`, is simply the same shape as the source sentence but has a value of 1 when the token in the source sentence is not a `<pad>` token and 0 when it is a `<pad>` token. This is used in the encoder layers to mask the multi-head attention mechanisms, which are used to calculate and apply attention over the source sentence, so the model does not pay attention to `<pad>` tokens, which contain no useful information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Encoder(nn.Module):\n",
    "    def __init__(self, \n",
    "                 input_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim,\n",
    "                 dropout, \n",
    "                 device,\n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "\n",
    "        self.device = device\n",
    "        \n",
    "        self.tok_embedding = nn.Embedding(input_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.ModuleList([EncoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim,\n",
    "                                                  dropout, \n",
    "                                                  device) \n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
    "        \n",
    "    def forward(self, src, src_mask):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #src_mask = [batch size, src len]\n",
    "        \n",
    "        batch_size = src.shape[0]\n",
    "        src_len = src.shape[1]\n",
    "        \n",
    "        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "        \n",
    "        #pos = [batch size, src len]\n",
    "        \n",
    "        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            src = layer(src, src_mask)\n",
    "            \n",
    "        #src = [batch size, src len, hid dim]\n",
    "            \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoder Layer\n",
    "\n",
    "The encoder layers are where all of the \"meat\" of the encoder is contained. We first pass the source sentence and its mask into the *multi-head attention layer*, then perform dropout on it, apply a residual connection and pass it through a [Layer Normalization](https://arxiv.org/abs/1607.06450) layer. We then pass it through a *position-wise feedforward* layer and then, again, apply dropout, a residual connection and then layer normalization to get the output of this layer which is fed into the next layer. The parameters are not shared between layers. \n",
    "\n",
    "The mutli head attention layer is used by the encoder layer to attend to the source sentence, i.e. it is calculating and applying attention over itself instead of another sequence, hence we call it *self attention*.\n",
    "\n",
    "[This](https://mlexplained.com/2018/01/13/weight-normalization-and-layer-normalization-explained-normalization-in-deep-learning-part-2/) article goes into more detail about layer normalization, but the gist is that it normalizes the values of the features, i.e. across the hidden dimension, so each feature has a mean of 0 and a standard deviation of 1. This allows neural networks with a larger number of layers, like the Transformer, to be trained easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class EncoderLayer(nn.Module):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim,  \n",
    "                 dropout, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, src, src_mask):\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        #src_mask = [batch size, src len]\n",
    "                \n",
    "        #self attention\n",
    "        _src, _ = self.self_attention(src, src, src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        src = self.layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _src = self.positionwise_feedforward(src)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        src = self.layer_norm(src + self.dropout(_src))\n",
    "        \n",
    "        #src = [batch size, src len, hid dim]\n",
    "        \n",
    "        return src"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutli Head Attention Layer\n",
    "\n",
    "One of the key, novel concepts introduced by the Transformer paper is the *multi-head attention layer*. \n",
    "\n",
    "![](assets/transformer-attention.png)\n",
    "\n",
    "Attention can be though of as *queries*, *keys* and *values* - where the query is used with the key to get an attention vector (usually the output of a *softmax* operation and has all values between 0 and 1 which sum to 1) which is then used to get a weighted sum of the values.\n",
    "\n",
    "The Transformer uses *scaled dot-product attention*, where the query and key are combined by taking the dot product between them, then applying the softmax operation and scaling by $d_k$ before finally then multiplying by the value. $d_k$ is the *head dimension*, `head_dim`, which we will shortly explain further.\n",
    "\n",
    "$$ \\text{Attention}(Q, K, V) = \\text{Softmax} \\big( \\frac{QK^T}{\\sqrt{d_k}} \\big)V $$ \n",
    "\n",
    "This is similar to standard *dot product attention* but is scaled by $d_k$, which the paper states is used to stop the results of the dot products growing large, causing gradients to become too small.\n",
    "\n",
    "However, the scaled dot-product attention isn't simply applied to the queries, keys and values. Instead of doing a single attention application the queries, keys and values have their `hid_dim` split into $h$ *heads* and the scaled dot-product attention is calculated over all heads in parallel. This means instead of paying attention to one concept per attention application, we pay attention to $h$. We then re-combine the heads into their `hid_dim` shape, thus each `hid_dim` is potentially paying attention to $h$ different concepts.\n",
    "\n",
    "$$ \\text{MultiHead}(Q, K, V) = \\text{Concat}(\\text{head}_1,...,\\text{head}_h)W^O $$\n",
    "\n",
    "$$\\text{head}_i = \\text{Attention}(QW_i^Q, KW_i^K, VW_i^V) $$\n",
    "\n",
    "$W^O$ is the linear layer applied at the end of the multi-head attention layer, `fc`. $W^Q, W^K, W^V$ are the linear layers `fc_q`, `fc_k` and `fc_v`.\n",
    "\n",
    "Walking through the module, first we calculate $QW^Q$, $KW^K$ and $VW^V$ with the linear layers, `fc_q`, `fc_k` and `fc_v`, to give us `Q`, `K` and `V`. Next, we split the `hid_dim` of the query, key and value into `n_heads` using `.view` and correctly permute them so they can be multiplied together. We then calculate the `energy` (the un-normalized attention) by multiplying `Q` and `K` together and scaling it by the square root of `head_dim`, which is calulated as `hid_dim // n_heads`. We then mask the energy so we do not pay attention over any elements of the sequeuence we shouldn't, then apply the softmax and dropout. We then apply the attention to the value heads, `V`, before combining the `n_heads` together. Finally, we multiply this $W^O$, represented by `fc_o`. \n",
    "\n",
    "Note that in our implementation the lengths of the keys and values are always the same, thus when matrix multiplying the output of the softmax, `attention`, with `V` we will always have valid dimension sizes for matrix multiplication. This multiplication is carried out using `torch.matmul` which, when both tensors are >2-dimensional, does a batched matrix multiplication over the last two dimensions of each tensor. This will be a **[query len, key len] x [value len, head dim]** batched matrix multiplication over the batch size and each head which provides the **[batch size, n heads, query len, head dim]** result.\n",
    "\n",
    "One thing that looks strange at first is that dropout is applied directly to the attention. This means that our attention vector will most probably not sum to 1 and we may pay full attention to a token but the attention over that token is set to 0 by dropout. This is never explained, or even mentioned, in the paper however is used by the [official implementation](https://github.com/tensorflow/tensor2tensor/) and every Transformer implementation since, [including BERT](https://github.com/google-research/bert/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttentionLayer(nn.Module):\n",
    "    def __init__(self, hid_dim, n_heads, dropout, device):\n",
    "        super().__init__()\n",
    "        \n",
    "        assert hid_dim % n_heads == 0\n",
    "        \n",
    "        self.hid_dim = hid_dim\n",
    "        self.n_heads = n_heads\n",
    "        self.head_dim = hid_dim // n_heads\n",
    "        \n",
    "        self.fc_q = nn.Linear(hid_dim, hid_dim)\n",
    "        self.fc_k = nn.Linear(hid_dim, hid_dim)\n",
    "        self.fc_v = nn.Linear(hid_dim, hid_dim)\n",
    "        \n",
    "        self.fc_o = nn.Linear(hid_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)\n",
    "        \n",
    "    def forward(self, query, key, value, mask = None):\n",
    "        \n",
    "        batch_size = query.shape[0]\n",
    "        \n",
    "        #query = [batch size, query len, hid dim]\n",
    "        #key = [batch size, key len, hid dim]\n",
    "        #value = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = self.fc_q(query)\n",
    "        K = self.fc_k(key)\n",
    "        V = self.fc_v(value)\n",
    "        \n",
    "        #Q = [batch size, query len, hid dim]\n",
    "        #K = [batch size, key len, hid dim]\n",
    "        #V = [batch size, value len, hid dim]\n",
    "                \n",
    "        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)\n",
    "        \n",
    "        #Q = [batch size, n heads, query len, head dim]\n",
    "        #K = [batch size, n heads, key len, head dim]\n",
    "        #V = [batch size, n heads, value len, head dim]\n",
    "                \n",
    "        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale\n",
    "        \n",
    "        #energy = [batch size, n heads, query len, key len]\n",
    "        \n",
    "        if mask is not None:\n",
    "            energy = energy.masked_fill(mask == 0, -1e10)\n",
    "        \n",
    "        attention = torch.softmax(energy, dim = -1)\n",
    "                \n",
    "        #attention = [batch size, n heads, query len, key len]\n",
    "                \n",
    "        x = torch.matmul(self.dropout(attention), V)\n",
    "        \n",
    "        #x = [batch size, n heads, query len, head dim]\n",
    "        \n",
    "        x = x.permute(0, 2, 1, 3).contiguous()\n",
    "        \n",
    "        #x = [batch size, query len, n heads, head dim]\n",
    "        \n",
    "        x = x.view(batch_size, -1, self.hid_dim)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        x = self.fc_o(x)\n",
    "        \n",
    "        #x = [batch size, query len, hid dim]\n",
    "        \n",
    "        return x, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Position-wise Feedforward Layer\n",
    "\n",
    "The other main block inside the encoder layer is the *position-wise feedforward layer* This is relatively simple compared to the multi-head attention layer. The input is transformed from `hid_dim` to `pf_dim`, where `pf_dim` is usually a lot larger than `hid_dim`. The original Transformer used a `hid_dim` of 512 and a `pf_dim` of 2048. The ReLU activation function and dropout are applied before it is transformed back into a `hid_dim` representation. \n",
    "\n",
    "Why is this used? Unfortunately, it is never explained in the paper.\n",
    "\n",
    "BERT uses the [GELU](https://arxiv.org/abs/1606.08415) activation function, which can be used by simply switching `torch.relu` for `F.gelu`. Why did they use GELU? Again, it is never explained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "class PositionwiseFeedforwardLayer(nn.Module):\n",
    "    def __init__(self, hid_dim, pf_dim, dropout):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.fc_1 = nn.Linear(hid_dim, pf_dim)\n",
    "        self.fc_2 = nn.Linear(pf_dim, hid_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        x = self.dropout(torch.relu(self.fc_1(x)))\n",
    "        \n",
    "        #x = [batch size, seq len, pf dim]\n",
    "        \n",
    "        x = self.fc_2(x)\n",
    "        \n",
    "        #x = [batch size, seq len, hid dim]\n",
    "        \n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder\n",
    "\n",
    "The objective of the decoder is to take the encoded representation of the source sentence, $Z$, and convert it into predicted tokens in the target sentence, $\\hat{Y}$. We then compare $\\hat{Y}$ with the actual tokens in the target sentence, $Y$, to calculate our loss, which will be used to calculate the gradients of our parameters and then use our optimizer to update our weights in order to improve our predictions. \n",
    "\n",
    "![](assets/transformer-decoder.png)\n",
    "\n",
    "The decoder is similar to encoder, however it now has two multi-head attention layers. A *masked multi-head attention layer* over the target sequence, and a multi-head attention layer which uses the decoder representation as the query and the encoder representation as the key and value.\n",
    "\n",
    "The decoder uses positional embeddings and combines - via an elementwise sum - them with the scaled embedded target tokens, followed by dropout. Again, our positional encodings have a \"vocabulary\" of 100, which means they can accept sequences up to 100 tokens long. This can be increased if desired.\n",
    "\n",
    "The combined embeddings are then passed through the $N$ decoder layers, along with the encoded source, `enc_src`, and the source and target masks. Note that the number of layers in the encoder does not have to be equal to the number of layers in the decoder, even though they are both denoted by $N$.\n",
    "\n",
    "The decoder representation after the $N^{th}$ layer is then passed through a linear layer, `fc_out`. In PyTorch, the softmax operation is contained within our loss function, so we do not explicitly need to use a softmax layer here.\n",
    "\n",
    "As well as using the source mask, as we did in the encoder to prevent our model attending to `<pad>` tokens, we also use a target mask. This will be explained further in the `Seq2Seq` model which encapsulates both the encoder and decoder, but the gist of it is that it performs a similar operation as the decoder padding in the convolutional sequence-to-sequence model. As we are processing all of the target tokens at once in parallel we need a method of stopping the decoder from \"cheating\" by simply \"looking\" at what the next token in the target sequence is and outputting it. \n",
    "\n",
    "Our decoder layer also outputs the normalized attention values so we can later plot them to see what our model is actually paying attention to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Decoder(nn.Module):\n",
    "    def __init__(self, \n",
    "                 output_dim, \n",
    "                 hid_dim, \n",
    "                 n_layers, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 device,\n",
    "                 max_length = 100):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.device = device\n",
    "        \n",
    "        self.tok_embedding = nn.Embedding(output_dim, hid_dim)\n",
    "        self.pos_embedding = nn.Embedding(max_length, hid_dim)\n",
    "        \n",
    "        self.layers = nn.ModuleList([DecoderLayer(hid_dim, \n",
    "                                                  n_heads, \n",
    "                                                  pf_dim, \n",
    "                                                  dropout, \n",
    "                                                  device)\n",
    "                                     for _ in range(n_layers)])\n",
    "        \n",
    "        self.fc_out = nn.Linear(hid_dim, output_dim)\n",
    "        \n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)\n",
    "        \n",
    "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, trg len]\n",
    "        #src_mask = [batch size, src len]\n",
    "                \n",
    "        batch_size = trg.shape[0]\n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)\n",
    "                            \n",
    "        #pos = [batch size, trg len]\n",
    "            \n",
    "        trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))\n",
    "                \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        for layer in self.layers:\n",
    "            trg, attention = layer(trg, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        output = self.fc_out(trg)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "            \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decoder Layer\n",
    "\n",
    "As mentioned previously, the decoder layer is similar to the encoder layer except that it now has two multi-head attention layers, `self_attention` and `encoder_attention`. \n",
    "\n",
    "The first performs self-attention, as in the encoder, by using the decoder representation so far as the query, key and value. This is followed by dropout, residual connection and layer normalization. This `self_attention` layer uses the target sequence mask, `trg_mask`, in order to prevent the decoder from \"cheating\" by paying attention to tokens that are \"ahead\" of the one it is currently processing as it processes all tokens in the target sentence in parallel.\n",
    "\n",
    "The second is how we actually feed the encoded source sentence, `enc_src`, into our decoder. In this multi-head attention layer the queries are the decoder representations and the keys and values are the decoder representations. Here, the source mask, `src_mask` is used to prevent the multi-head attention layer from attending to `<pad>` tokens within the source sentence. This is then followed by the dropout, residual connection and layer normalization layers. \n",
    "\n",
    "Finally, we pass this through the position-wise feedforward layer and yet another sequence of dropout, residual connection and layer normalization.\n",
    "\n",
    "The decoder layer isn't introducing any new concepts, just using the same set of layers as the encoder in a slightly different way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecoderLayer(nn.Module):\n",
    "    def __init__(self, \n",
    "                 hid_dim, \n",
    "                 n_heads, \n",
    "                 pf_dim, \n",
    "                 dropout, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.layer_norm = nn.LayerNorm(hid_dim)\n",
    "        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)\n",
    "        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, \n",
    "                                                                     pf_dim, \n",
    "                                                                     dropout)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, trg, enc_src, trg_mask, src_mask):\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "        #trg_mask = [batch size, trg len]\n",
    "        #src_mask = [batch size, src len]\n",
    "        \n",
    "        #self attention\n",
    "        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.layer_norm(trg + self.dropout(_trg))\n",
    "            \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "            \n",
    "        #encoder attention\n",
    "        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)\n",
    "        \n",
    "        #dropout, residual connection and layer norm\n",
    "        trg = self.layer_norm(trg + self.dropout(_trg))\n",
    "                    \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        \n",
    "        #positionwise feedforward\n",
    "        _trg = self.positionwise_feedforward(trg)\n",
    "        \n",
    "        #dropout, residual and layer norm\n",
    "        trg = self.layer_norm(trg + self.dropout(_trg))\n",
    "        \n",
    "        #trg = [batch size, trg len, hid dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return trg, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seq2Seq\n",
    "\n",
    "Finally, we have the `Seq2Seq` module which encapsulates the encoder and decoder, as well as handling the creation of the masks.\n",
    "\n",
    "The source mask is created by checking where the source sequence is not equal to a `<pad>` token. It is 1 where the token is not a `<pad>` token and 0 when it is. It is then unsqueezed so it can be correctly broadcast when applying the mask to the `energy`, which of shape **_[batch size, n heads, seq len, seq len]_**.\n",
    "\n",
    "The target mask is slightly more complicated. First, we create a mask for the `<pad>` tokens, as we did for the source mask. Next, we create a \"subsequent\" mask, `trg_sub_mask`, using `torch.tril`. This creates a diagonal matrix where the elements above the diagonal will be zero and the elements below the diagonal will be set to whatever the input tensor is. In this case, the input tensor will be a tensor filled with ones. So this means our `trg_sub_mask` will look something like this (for a target with 5 tokens):\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 0\\\\\n",
    "1 & 1 & 1 & 1 & 1\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "This shows what each target token (row) is allowed to look at (column). The first target token has a mask of **_[1, 0, 0, 0, 0]_** which means it can only look at the first target token. The second target token has a mask of **_[1, 1, 0, 0, 0]_** which it means it can look at both the first and second target tokens. \n",
    "\n",
    "The \"subsequent\" mask is then logically anded with the padding mask, this combines the two masks ensuring both the subsequent tokens and the padding tokens cannot be attended to. For example if the last two tokens were `<pad>` tokens the mask would look like:\n",
    "\n",
    "$$\\begin{matrix}\n",
    "1 & 0 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 0 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "1 & 1 & 1 & 0 & 0\\\\\n",
    "\\end{matrix}$$\n",
    "\n",
    "After the masks are created, they used with the encoder and decoder along with the source and target sentences to get our predicted target sentence, `output`, along with the decoder's attention over the source sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Seq2Seq(nn.Module):\n",
    "    def __init__(self, \n",
    "                 encoder, \n",
    "                 decoder, \n",
    "                 src_pad_idx, \n",
    "                 trg_pad_idx, \n",
    "                 device):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.src_pad_idx = src_pad_idx\n",
    "        self.trg_pad_idx = trg_pad_idx\n",
    "        self.device = device\n",
    "        \n",
    "    def make_src_mask(self, src):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        \n",
    "        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "\n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "\n",
    "        return src_mask\n",
    "    \n",
    "    def make_trg_mask(self, trg):\n",
    "        \n",
    "        #trg = [batch size, trg len]\n",
    "        \n",
    "        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)\n",
    "        \n",
    "        #trg_pad_mask = [batch size, 1, 1, trg len]\n",
    "        \n",
    "        trg_len = trg.shape[1]\n",
    "        \n",
    "        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device = self.device)).bool()\n",
    "        \n",
    "        #trg_sub_mask = [trg len, trg len]\n",
    "            \n",
    "        trg_mask = trg_pad_mask & trg_sub_mask\n",
    "        \n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        return trg_mask\n",
    "\n",
    "    def forward(self, src, trg):\n",
    "        \n",
    "        #src = [batch size, src len]\n",
    "        #trg = [batch size, trg len]\n",
    "                \n",
    "        src_mask = self.make_src_mask(src)\n",
    "        trg_mask = self.make_trg_mask(trg)\n",
    "        \n",
    "        #src_mask = [batch size, 1, 1, src len]\n",
    "        #trg_mask = [batch size, 1, trg len, trg len]\n",
    "        \n",
    "        enc_src = self.encoder(src, src_mask)\n",
    "        \n",
    "        #enc_src = [batch size, src len, hid dim]\n",
    "                \n",
    "        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        #output = [batch size, trg len, output dim]\n",
    "        #attention = [batch size, n heads, trg len, src len]\n",
    "        \n",
    "        return output, attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Seq2Seq Model\n",
    "\n",
    "We can now define our encoder and decoders. This model is significantly smaller than Transformers used in research today, but is able to be run on a single GPU quickly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_DIM = len(SRC.vocab)\n",
    "OUTPUT_DIM = len(TRG.vocab)\n",
    "HID_DIM = 256\n",
    "ENC_LAYERS = 3\n",
    "DEC_LAYERS = 3\n",
    "ENC_HEADS = 8\n",
    "DEC_HEADS = 8\n",
    "ENC_PF_DIM = 512\n",
    "DEC_PF_DIM = 512\n",
    "ENC_DROPOUT = 0.1\n",
    "DEC_DROPOUT = 0.1\n",
    "\n",
    "enc = Encoder(INPUT_DIM, \n",
    "              HID_DIM, \n",
    "              ENC_LAYERS, \n",
    "              ENC_HEADS, \n",
    "              ENC_PF_DIM, \n",
    "              ENC_DROPOUT, \n",
    "              device)\n",
    "\n",
    "dec = Decoder(OUTPUT_DIM, \n",
    "              HID_DIM, \n",
    "              DEC_LAYERS, \n",
    "              DEC_HEADS, \n",
    "              DEC_PF_DIM, \n",
    "              DEC_DROPOUT, \n",
    "              device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, use them to define our whole sequence-to-sequence encapsulating model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "SRC_PAD_IDX = SRC.vocab.stoi[SRC.pad_token]\n",
    "TRG_PAD_IDX = TRG.vocab.stoi[TRG.pad_token]\n",
    "\n",
    "model = Seq2Seq(enc, dec, SRC_PAD_IDX, TRG_PAD_IDX, device).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the number of parameters, noticing it is significantly less than the 37M for the convolutional sequence-to-sequence model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 10,409,240 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The paper does not mention which weight initialization scheme was used, however Xavier uniform seems to be common amongst Transformer models, so we use it here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def initialize_weights(m):\n",
    "    if hasattr(m, 'weight') and m.weight.dim() > 1:\n",
    "        nn.init.xavier_uniform_(m.weight.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.apply(initialize_weights);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The optimizer used in the original Transformer paper uses Adam with a learning rate that has a \"warm-up\" and then a \"cool-down\" period. BERT and other Transformer models use Adam with a fixed learning rate, so we will implement that. Check [this](http://nlp.seas.harvard.edu/2018/04/03/attention.html#optimizer) link for more details about the original Transformer's learning rate schedule.\n",
    "\n",
    "Note that the learning rate needs to be lower than the default used by Adam or else learning is unstable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "LEARNING_RATE = 0.0005\n",
    "\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr = LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define our loss function, making sure to ignore losses calculated over `<pad>` tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "criterion = nn.CrossEntropyLoss(ignore_index = TRG_PAD_IDX)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we'll define our training loop. This is the exact same as the one used in the previous tutorial.\n",
    "\n",
    "As we want our model to predict the `<eos>` token but not have it be an input into our model we simply slice the `<eos>` token off the end of the sequence. Thus:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{trg} &= [sos, x_1, x_2, x_3, eos]\\\\\n",
    "\\text{trg[:-1]} &= [sos, x_1, x_2, x_3]\n",
    "\\end{align*}$$\n",
    "\n",
    "$x_i$ denotes actual target sequence element. We then feed this into the model to get a predicted sequence that should hopefully predict the `<eos>` token:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "$y_i$ denotes predicted target sequence element. We then calculate our loss using the original `trg` tensor with the `<sos>` token sliced off the front, leaving the `<eos>` token:\n",
    "\n",
    "$$\\begin{align*}\n",
    "\\text{output} &= [y_1, y_2, y_3, eos]\\\\\n",
    "\\text{trg[1:]} &= [x_1, x_2, x_3, eos]\n",
    "\\end{align*}$$\n",
    "\n",
    "We then calculate our losses and update our parameters as is standard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, iterator, optimizer, criterion, clip):\n",
    "    \n",
    "    model.train()\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    \n",
    "    for i, batch in enumerate(iterator):\n",
    "        \n",
    "        src = batch.src\n",
    "        trg = batch.trg\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        \n",
    "        output, _ = model(src, trg[:,:-1])\n",
    "                \n",
    "        #output = [batch size, trg len - 1, output dim]\n",
    "        #trg = [batch size, trg len]\n",
    "            \n",
    "        output_dim = output.shape[-1]\n",
    "            \n",
    "        output = output.contiguous().view(-1, output_dim)\n",
    "        trg = trg[:,1:].contiguous().view(-1)\n",
    "                \n",
    "        #output = [batch size * trg len - 1, output dim]\n",
    "        #trg = [batch size * trg len - 1]\n",
    "            \n",
    "        loss = criterion(output, trg)\n",
    "        \n",
    "        loss.backward()\n",
    "        \n",
    "        torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
    "        \n",
    "        optimizer.step()\n",
    "        \n",
    "        epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The evaluation loop is the same as the training loop, just without the gradient calculations and parameter updates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, iterator, criterion):\n",
    "    \n",
    "    model.eval()\n",
    "    \n",
    "    epoch_loss = 0\n",
    "    \n",
    "    with torch.no_grad():\n",
    "    \n",
    "        for i, batch in enumerate(iterator):\n",
    "\n",
    "            src = batch.src\n",
    "            trg = batch.trg\n",
    "\n",
    "            output, _ = model(src, trg[:,:-1])\n",
    "            \n",
    "            #output = [batch size, trg len - 1, output dim]\n",
    "            #trg = [batch size, trg len]\n",
    "            \n",
    "            output_dim = output.shape[-1]\n",
    "            \n",
    "            output = output.contiguous().view(-1, output_dim)\n",
    "            trg = trg[:,1:].contiguous().view(-1)\n",
    "            \n",
    "            #output = [batch size * trg len - 1, output dim]\n",
    "            #trg = [batch size * trg len - 1]\n",
    "            \n",
    "            loss = criterion(output, trg)\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "        \n",
    "    return epoch_loss / len(iterator)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then define a small function that we can use to tell us how long an epoch takes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def epoch_time(start_time, end_time):\n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we train our actual model. This model is almost 3x faster than the convolutional sequence-to-sequence model and also achieves a lower validation perplexity!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "",
      "KeyboardInterruptTraceback (most recent call last)",
      "<ipython-input-39-eff3342c78df> in <module>()\n      8     start_time = time.time()\n      9     \n---> 10     train_loss = train(model, train_iterator, optimizer, criterion, CLIP)\n     11     valid_loss = evaluate(model, valid_iterator, criterion)\n     12     \n",
      "<ipython-input-35-1d15634f42f4> in train(model, iterator, optimizer, criterion, clip)\n     27         loss = criterion(output, trg)\n     28         \n---> 29         loss.backward()\n     30         \n     31         torch.nn.utils.clip_grad_norm_(model.parameters(), clip)\n",
      "/Users/xuming06/Library/Python/3.6/lib/python/site-packages/torch/tensor.py in backward(self, gradient, retain_graph, create_graph)\n    193                 products. Defaults to ``False``.\n    194         \"\"\"\n--> 195         torch.autograd.backward(self, gradient, retain_graph, create_graph)\n    196 \n    197     def register_hook(self, hook):\n",
      "/Users/xuming06/Library/Python/3.6/lib/python/site-packages/torch/autograd/__init__.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\n     97     Variable._execution_engine.run_backward(\n     98         tensors, grad_tensors, retain_graph, create_graph,\n---> 99         allow_unreachable=True)  # allow_unreachable flag\n    100 \n    101 \n",
      "KeyboardInterrupt: "
     ]
    }
   ],
   "source": [
    "N_EPOCHS = 1\n",
    "CLIP = 1\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    \n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss = train(model, train_iterator, optimizer, criterion, CLIP)\n",
    "    valid_loss = evaluate(model, valid_iterator, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "    \n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'tut6-model.pt')\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. PPL: {math.exp(valid_loss):7.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load our \"best\" parameters and manage to achieve a better test perplexity than all previous models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| Test Loss: 1.662 | Test PPL:   5.268 |\n"
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('tut6-model.pt'))\n",
    "\n",
    "test_loss = evaluate(model, test_iterator, criterion)\n",
    "\n",
    "print(f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference\n",
    "\n",
    "Now we can can translations from our model with the `translate_sentence` function below.\n",
    "\n",
    "The steps taken are:\n",
    "- tokenize the source sentence if it has not been tokenized (is a string)\n",
    "- append the `<sos>` and `<eos>` tokens\n",
    "- numericalize the source sentence\n",
    "- convert it to a tensor and add a batch dimension\n",
    "- create the source sentence mask\n",
    "- feed the source sentence and mask into the encoder\n",
    "- create a list to hold the output sentence, initialized with an `<sos>` token\n",
    "- while we have not hit a maximum length\n",
    "  - convert the current output sentence prediction into a tensor with a batch dimension\n",
    "  - create a target sentence mask\n",
    "  - place the current output, encoder output and both masks into the decoder\n",
    "  - get next output token prediction from decoder along with attention\n",
    "  - add prediction to current output sentence prediction\n",
    "  - break if the prediction was an `<eos>` token\n",
    "- convert the output sentence from indexes to tokens\n",
    "- return the output sentence (with the `<sos>` token removed) and the attention from the last layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):\n",
    "    \n",
    "    model.eval()\n",
    "        \n",
    "    if isinstance(sentence, str):\n",
    "        nlp = spacy.load('de')\n",
    "        tokens = [token.text.lower() for token in nlp(sentence)]\n",
    "    else:\n",
    "        tokens = [token.lower() for token in sentence]\n",
    "\n",
    "    tokens = [src_field.init_token] + tokens + [src_field.eos_token]\n",
    "        \n",
    "    src_indexes = [src_field.vocab.stoi[token] for token in tokens]\n",
    "\n",
    "    src_tensor = torch.LongTensor(src_indexes).unsqueeze(0).to(device)\n",
    "    \n",
    "    src_mask = model.make_src_mask(src_tensor)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        enc_src = model.encoder(src_tensor, src_mask)\n",
    "\n",
    "    trg_indexes = [trg_field.vocab.stoi[trg_field.init_token]]\n",
    "\n",
    "    for i in range(max_len):\n",
    "\n",
    "        trg_tensor = torch.LongTensor(trg_indexes).unsqueeze(0).to(device)\n",
    "\n",
    "        trg_mask = model.make_trg_mask(trg_tensor)\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            output, attention = model.decoder(trg_tensor, enc_src, trg_mask, src_mask)\n",
    "        \n",
    "        pred_token = output.argmax(2)[:,-1].item()\n",
    "        \n",
    "        trg_indexes.append(pred_token)\n",
    "\n",
    "        if pred_token == trg_field.vocab.stoi[trg_field.eos_token]:\n",
    "            break\n",
    "    \n",
    "    trg_tokens = [trg_field.vocab.itos[i] for i in trg_indexes]\n",
    "    \n",
    "    return trg_tokens[1:], attention"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now define a function that displays the attention over the source sentence for each step of the decoding. As this model has 8 heads our model we can view the attention for each of the heads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def display_attention(sentence, translation, attention, n_heads = 8, n_rows = 4, n_cols = 2):\n",
    "    \n",
    "    assert n_rows * n_cols == n_heads\n",
    "    \n",
    "    fig = plt.figure(figsize=(15,25))\n",
    "    \n",
    "    for i in range(n_heads):\n",
    "        \n",
    "        ax = fig.add_subplot(n_rows, n_cols, i+1)\n",
    "        \n",
    "        _attention = attention.squeeze(0)[i].cpu().detach().numpy()\n",
    "\n",
    "        cax = ax.matshow(_attention, cmap='bone')\n",
    "\n",
    "        ax.tick_params(labelsize=12)\n",
    "        ax.set_xticklabels(['']+['<sos>']+[t.lower() for t in sentence]+['<eos>'], \n",
    "                           rotation=45)\n",
    "        ax.set_yticklabels(['']+translation)\n",
    "\n",
    "        ax.xaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "        ax.yaxis.set_major_locator(ticker.MultipleLocator(1))\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we'll get an example from the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['eine', 'frau', 'mit', 'einer', 'großen', 'geldbörse', 'geht', 'an', 'einem', 'tor', 'vorbei', '.']\n",
      "trg = ['a', 'woman', 'with', 'a', 'large', 'purse', 'is', 'walking', 'by', 'a', 'gate', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 8\n",
    "\n",
    "src = vars(train_data.examples[example_idx])['src']\n",
    "trg = vars(train_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our translation looks pretty good, although our model changes *is walking by* to *walks past*. The meaning is still the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'woman', 'with', 'a', 'large', 'purse', 'walks', 'past', 'a', 'gate', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see the attention from each head below. Each is certainly different, but it's difficult (perhaps impossible) to reason about what head has actually learned to pay attention to. Some heads pay full attention to \"eine\" when translating \"a\", some don't at all, and some do a little. They all seem to follow the similar \"downward staircase\" pattern and the attention when outputting the last two tokens is equally spread over the final two tokens in the input sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, let's get an example the model has not been trained on from the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['ein', 'brauner', 'hund', 'rennt', 'dem', 'schwarzen', 'hund', 'hinterher', '.']\n",
      "trg = ['a', 'brown', 'dog', 'is', 'running', 'after', 'the', 'black', 'dog', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 6\n",
    "\n",
    "src = vars(valid_data.examples[example_idx])['src']\n",
    "trg = vars(valid_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model translates it by switching *is running* to just *running*, but it is an acceptable swap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'brown', 'dog', 'running', 'after', 'the', 'black', 'dog', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, some heads pay full attention to \"ein\" whilst some pay no attention to it. Again, most of the heads seem to spread their attention over both the period and `<eos>` tokens in the source sentence when outputting the period and `<eos>` sentence in the predicted target sentence, though some seem to pay attention to tokens from near the start of the sentence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we'll look at an example from the test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "src = ['eine', 'mutter', 'und', 'ihr', 'kleiner', 'sohn', 'genießen', 'einen', 'schönen', 'tag', 'im', 'freien', '.']\n",
      "trg = ['a', 'mother', 'and', 'her', 'young', 'song', 'enjoying', 'a', 'beautiful', 'day', 'outside', '.']\n"
     ]
    }
   ],
   "source": [
    "example_idx = 10\n",
    "\n",
    "src = vars(test_data.examples[example_idx])['src']\n",
    "trg = vars(test_data.examples[example_idx])['trg']\n",
    "\n",
    "print(f'src = {src}')\n",
    "print(f'trg = {trg}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A decent translation with *young* being omitted and *outside* being changed to *outdoors*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted trg = ['a', 'mother', 'and', 'her', 'son', 'enjoying', 'a', 'beautiful', 'day', 'outdoors', '.', '<eos>']\n"
     ]
    }
   ],
   "source": [
    "translation, attention = translate_sentence(src, SRC, TRG, model, device)\n",
    "\n",
    "print(f'predicted trg = {translation}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_attention(src, translation, attention)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BLEU\n",
    "\n",
    "Finally we calculate the BLEU score for the Transformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchtext.data.metrics import bleu_score\n",
    "\n",
    "def calculate_bleu(data, src_field, trg_field, model, device, max_len = 50):\n",
    "    \n",
    "    trgs = []\n",
    "    pred_trgs = []\n",
    "    \n",
    "    for datum in data:\n",
    "        \n",
    "        src = vars(datum)['src']\n",
    "        trg = vars(datum)['trg']\n",
    "        \n",
    "        pred_trg, _ = translate_sentence(src, src_field, trg_field, model, device, max_len)\n",
    "        \n",
    "        #cut off <eos> token\n",
    "        pred_trg = pred_trg[:-1]\n",
    "        \n",
    "        pred_trgs.append(pred_trg)\n",
    "        trgs.append([trg])\n",
    "        \n",
    "    return bleu_score(pred_trgs, trgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a BLEU score of 36.1, which beats the 33.3 of the convolutional sequence-to-sequence model and 28.2 of the attention based RNN model. All this whilst having the least amount of parameters and the fastest training time!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BLEU score = 36.09\n"
     ]
    }
   ],
   "source": [
    "bleu_score = calculate_bleu(test_data, SRC, TRG, model, device)\n",
    "\n",
    "print(f'BLEU score = {bleu_score*100:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congratulations for finishing these tutorials! I hope you've found them useful.\n",
    "\n",
    "If you find any mistakes or want to ask any questions about any of the code or explanations used, feel free to submit a GitHub issue and I will try to correct it ASAP. "
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
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 },
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